A Pixel-Encoder Retinal Ganglion Cell with Spatially Offset Excitatory and Inhibitory Receptive Fields

Gap junctions fine-tune ganglion cell signals to equalize response kinetics within a given electrically coupled array

Retinal ganglion cells (RGCs) summate inputs and forward a spike train code to the brain in the form of either maintained spiking (sustained) or a quickly decaying brief spike burst (transient). We report diverse response transience values across the RGC population and, contrary to the conventional transient/sustained scheme, responses with intermediary characteristics are the most abundant. Pharmacological tests showed that besides GABAergic inhibition, gap junction (GJ)-mediated excitation also plays a pivotal role in shaping response transience and thus visual coding. More precisely GJs connecting RGCs to nearby amacrine and RGCs play a defining role in the process. These GJs equalize kinetic features, including the response transience of transient OFF alpha (tOFFα) RGCs across a coupled array. We propose that GJs in other coupled neuron ensembles in the brain are also critical in the harmonization of response kinetics to enhance the population code and suit a corresponding task.

Parallel pathways carrying direction-and orientation-selective retinal signals to layer 4 of the mouse visual cortex

Parallel visual pathways from the retina to the primary visual cortex (V1) via the lateral geniculate nucleus are common to many mammalian species, including mice, carnivores, and primates. However, it remains unclear which visual features present in both retina and V1 may be inherited from parallel pathways versus extracted by V1 circuits in the mouse. Here, using calcium imaging and rabies circuit tracing, we explore the relationships between tuning of layer 4 (L4) V1 neurons and their retinal ganglion cell (RGC) inputs. We find that subpopulations of L4 V1 neurons differ in their tuning for direction, orientation, spatial frequency, temporal frequency, and speed. Furthermore, we find that direction-tuned L4 V1 neurons receive input from direction-selective RGCs, whereas orientation-tuned L4 V1 neurons receive input from orientation-selective RGCs. These results suggest that direction and orientation tuning of V1 neurons may be partly inherited from parallel pathways originating in the retina.

Mapping the Retina onto the Brain

Vision begins in the retina, which extracts salient features from the environment and encodes them in the spike trains of retinal ganglion cells (RGCs), the output neurons of the eye. RGC axons innervate diverse brain areas (>50 in mice) to support perception, guide behavior, and mediate influences of light on physiology and internal states. In recent years, complete lists of RGC types (∼45 in mice) have been compiled, detailed maps of their dendritic connections drawn, and their light responses surveyed at scale. We know less about the RGCs' axonal projection patterns, which map retinal information onto the brain. However, some organizing principles have emerged. Here, we review the strategies and mechanisms that govern developing RGC axons and organize their innervation of retinorecipient brain areas.

A presynaptic source drives differing levels of surround suppression in two mouse retinal ganglion cell types

In early sensory systems, cell-type diversity generally increases from the periphery into the brain, resulting in a greater heterogeneity of responses to the same stimuli. Surround suppression is a canonical visual computation that begins within the retina and is found at varying levels across retinal ganglion cell types. Our results show that heterogeneity in the level of surround suppression occurs subcellularly at bipolar cell synapses. Using single-cell electrophysiology and serial block-face scanning electron microscopy, we show that two retinal ganglion cell types exhibit very different levels of surround suppression even though they receive input from the same bipolar cell types. This divergence of the bipolar cell signal occurs through synapse-specific regulation by amacrine cells at the scale of tens of microns. These findings indicate that each synapse of a single bipolar cell can carry a unique visual signal, expanding the number of possible functional channels at the earliest stages of visual processing.

Glutamate transporter contribution to retinal ganglion cell vulnerability in a rat model of multiple sclerosis

Glial glutamate transporters actively participate in neurotransmission and have a fundamental role in determining the ambient glutamate concentration in the extracellular space. Their expression is dynamically regulated in many diseases, including experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis. In EAE, a downregulation has been reported which may render neurons more susceptible to glutamate excitotoxicity. In this study, we have investigated the expression of GLAST (EAAT1) and GLT-1 (EAAT2) in the retina of Brown Norway rats following induction of myelin oligodendrocyte glycoprotein (MOG)-EAE, which results in retinal ganglion cell (RGC) degeneration and dysfunction. In addition, we tested whether AAV-mediated overexpression of GLAST in the retina can protect RGCs from degeneration. To address the impact of glutamate transporter modulation on RGCs, we performed whole-cell recordings and measured tonic NMDA receptor-mediated currents in the absence and presence of a glutamate-uptake blocker. We report that αOFF-RGCs show larger tonic glutamate-induced currents than αON-RGCs, in line with their greater vulnerability under neuroinflammatory conditions. We further show that increased AAV-mediated expression of GLAST in the retina does indeed protect RGCs from degeneration during the inflammatory disease. Collectively, our study highlights the neuroprotective role of glutamate transporters in the EAE retina and provides a characterization of tonic glutamate-currents of αRGCs. The larger effects of increased extracellular glutamate concentration on the αOFF-subtype may underlie its enhanced vulnerability to degeneration.

Parallel pathways carrying direction and orientation selective retinal signals to layer 4 of mouse visual cortex

Parallel functional and anatomical visual pathways from the retina to primary visual cortex (V1) via the lateral geniculate nucleus (LGN) are common to many mammalian species, including mice, carnivores and primates. However, the much larger number of retinal ganglion cell (RGC) types that project to the LGN, as well as the more limited lamination of both the LGN and the thalamocortical-recipient layer 4 (L4) in mice, leaves considerable uncertainty about which visual features present in both retina and V1 might be inherited from parallel pathways versus extracted by V1 circuits in the mouse visual system. Here, we explored the relationships between functional properties of L4 V1 neurons and their RGC inputs by taking advantage of two Cre-expressing mouse lines - Nr5a1-Cre and Scnn1a-Tg3-Cre - that each label functionally and anatomically distinct populations of L4 neurons. Visual tuning properties of L4 V1 neurons were evaluated using Cre-dependent expression of GCaMP6s followed by 2-photon calcium imaging. RGCs providing input to these neurons (via LGN) were labeled and characterized using Cre-dependent trans-synaptic retrograde labeling with G-deleted rabies virus. We find significant differences in the tuning of Nr5a1-Cre versus Scnn1a-Tg3-Cre neurons for direction, orientation, spatial frequency, temporal frequency, and speed. Strikingly, a subset of the RGCs had tuning properties that matched the direction and orientation tuning properties of the L4 V1 neurons to which they provided input. Altogether, these results suggest that direction and orientation tuning of V1 neurons may be at least partly inherited from parallel pathways originating in the retina.

Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression

Classification and characterization of neuronal types are critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological, and molecular features, but aligning such datasets has been challenging. Here, we present a unified classification of mouse retinal ganglion cells (RGCs), the sole retinal output neurons. We use visually evoked responses to classify 1,859 mouse RGCs into 42 types. We also obtain morphological or transcriptomic data from subsets and use these measurements to align the functional classification to publicly available morphological and transcriptomic datasets. We create an online database that allows users to browse or download the data and to classify RGCs from their light responses using a machine learning algorithm. This work provides a resource for studies of RGCs, their upstream circuits in the retina, and their projections in the brain, and establishes a framework for future efforts in neuronal classification and open data distribution.

Differences in spike generation instead of synaptic inputs determine the feature selectivity of two retinal cell types

Retinal ganglion cells (RGCs) are the spiking projection neurons of the eye that encode different features of the visual environment. The circuits providing synaptic input to different RGC types to drive feature selectivity have been studied extensively, but there has been less research aimed at understanding the intrinsic properties and how they impact feature selectivity. We introduce an RGC type in the mouse, the Bursty Suppressed-by-Contrast (bSbC) RGC, and compared it to the OFF sustained alpha (OFFsA). Differences in their contrast response functions arose from differences not in synaptic inputs but in their intrinsic properties. Spike generation was the key intrinsic property behind this functional difference; the bSbC RGC undergoes depolarization block while the OFFsA RGC maintains a high spike rate. Our results demonstrate that differences in intrinsic properties allow these two RGC types to detect and relay distinct features of an identical visual stimulus to the brain.

Tools and Biomarkers for the Study of Retinal Ganglion Cell Degeneration

The retina is part of the central nervous system, its analysis may provide an idea of the health and functionality, not only of the retina, but also of the entire central nervous system, as has been shown in Alzheimer’s or Parkinson’s diseases. Within the retina, the ganglion cells (RGC) are the neurons in charge of processing and sending light information to higher brain centers. Diverse insults and pathological states cause degeneration of RGC, leading to irreversible blindness or impaired vision. RGCs are the measurable endpoints in current research into experimental therapies and diagnosis in multiple ocular pathologies, like glaucoma. RGC subtype classifications are based on morphological, functional, genetical, and immunohistochemical aspects. Although great efforts are being made, there is still no classification accepted by consensus. Moreover, it has been observed that each RGC subtype has a different susceptibility to injury. Characterizing these subtypes together with cell death pathway identification will help to understand the degenerative process in the different injury and pathological models, and therefore prevent it. Here we review the known RGC subtypes, as well as the diagnostic techniques, probes, and biomarkers for programmed and unprogrammed cell death in RGC.

Feature Detection by Retinal Ganglion Cells

Retinal circuits transform the pixel representation of photoreceptors into the feature representations of ganglion cells, whose axons transmit these representations to the brain. Functional, morphological, and transcriptomic surveys have identified more than 40 retinal ganglion cell (RGC) types in mice. RGCs extract features of varying complexity; some simply signal local differences in brightness (i.e., luminance contrast), whereas others detect specific motion trajectories. To understand the retina, we need to know how retinal circuits give rise to the diverse RGC feature representations. A catalog of the RGC feature set, in turn, is fundamental to understanding visual processing in the brain. Anterograde tracing indicates that RGCs innervate more than 50 areas in the mouse brain. Current maps connecting RGC types to brain areas are rudimentary, as is our understanding of how retinal signals are transformed downstream to guide behavior. In this article, I review the feature selectivities of mouse RGCs, how they arise, and how they are utilized downstream. Not only is knowledge of the behavioral purpose of RGC signals critical for understanding the retinal contributions to vision; it can also guide us to the most relevant areas of visual feature space. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

An electroactive hybrid biointerface for enhancing neuronal differentiation and axonal outgrowth on bio-subretinal chip

Retinal prostheses offer viable vision restoration therapy for patients with blindness. However, a critical requirement for maintaining the stable performance of electrical stimulation and signal transmission is the biocompatibility of the electrode interface. Here, we demonstrated a functionalized electrode-neuron biointerface composed of an annealed graphene oxide-collagen (aGO-COL) composite and neuronal cells. The aGO-COL exhibited an electroactive 3D crumpled surface structure and enhanced the differentiation efficiency of PC-12 ​cells. It is integrated into a photovoltaic self-powered retinal chip to develop a biohybrid retinal implant that facilitates biocompatibility and tissue regeneration. Moreover, aGO-COL micropatterns fabricated via 3D bioprinting can be used to create neuronal cell microarrays, which supports the possibility of retaining the high spatial resolution achieved through electrical stimulation of the retinal chip. This study paves the way for the next generation of biohybrid retinal implants based on biointerfaces.

A novel approach to the functional classification of retinal ganglion cells

Retinal neurons are remarkedly diverse based on structure, function and genetic identity. Classifying these cells is a challenging task, requiring multimodal methodology. Here, we introduce a novel approach for retinal ganglion cell (RGC) classification, based on pharmacogenetics combined with immunohistochemistry and large-scale retinal electrophysiology. Our novel strategy allows grouping of cells sharing gene expression and understanding how these cell classes respond to basic and complex visual scenes. Our approach consists of several consecutive steps. First, the spike firing frequency is increased in RGCs co-expressing a certain gene (Scnn1a or Grik4) using excitatory DREADDs (designer receptors exclusively activated by designer drugs) in order to single out activity originating specifically from these cells. Their spike location is then combined with post hoc immunostaining, to unequivocally characterize their anatomical and functional features. We grouped these isolated RGCs into multiple clusters based on spike train similarities. Using this novel approach, we were able to extend the pre-existing list of Grik4-expressing RGC types to a total of eight and, for the first time, we provide a phenotypical description of 13 Scnn1a-expressing RGCs. The insights and methods gained here can guide not only RGC classification but neuronal classification challenges in other brain regions as well.

Transience of the Retinal Output Is Determined by a Great Variety of Circuit Elements

Retinal ganglion cells (RGCs) encrypt stimulus features of the visual scene in action potentials and convey them toward higher visual centers in the brain. Although there are many visual features to encode, our recent understanding is that the ~46 different functional subtypes of RGCs in the retina share this task. In this scheme, each RGC subtype establishes a separate, parallel signaling route for a specific visual feature (e.g., contrast, the direction of motion, luminosity), through which information is conveyed. The efficiency of encoding depends on several factors, including signal strength, adaptational levels, and the actual efficacy of the underlying retinal microcircuits. Upon collecting inputs across their respective receptive field, RGCs perform further analysis (e.g., summation, subtraction, weighting) before they generate the final output spike train, which itself is characterized by multiple different features, such as the number of spikes, the inter-spike intervals, response delay, and the rundown time (transience) of the response. These specific kinetic features are essential for target postsynaptic neurons in the brain in order to effectively decode and interpret signals, thereby forming visual perception. We review recent knowledge regarding circuit elements of the mammalian retina that participate in shaping RGC response transience for optimal visual signaling.

Genetic interplay between transcription factor Pou4f1/Brn3a and neurotrophin receptor Ret in retinal ganglion cell type specification

Background

While the transcriptional code governing retinal ganglion cell (RGC) type specification begins to be understood, its interplay with neurotrophic signaling is largely unexplored. In mice, the transcription factor Brn3a/Pou4f1 is expressed in most RGCs, and is required for the specification of RGCs with small dendritic arbors. The Glial Derived Neurotrophic Factor (GDNF) receptor Ret is expressed in a subset of RGCs, including some expressing Brn3a, but its role in RGC development is not defined.

Methods

Here we use combinatorial genetic experiments using conditional knock-in reporter alleles at the Brn3a and Ret loci, in combination with retina- or Ret specific Cre drivers, to generate complete or mosaic genetic ablations of either Brn3a or Ret in RGCs. We then use sparse labelling to investigate Brn3a and Ret gene dosage effects on RGC dendritic arbor morphology. In addition, we use immunostaining and/or gene expression profiling by RNASeq to identify transcriptional targets relevant for the potential Brn3a-Ret interaction in RGC development.

Results

We find that mosaic gene dosage manipulation of the transcription factor Brn3a/Pou4f1 in neurotrophic receptor Ret heterozygote RGCs results in altered cell fate decisions and/or morphological dendritic defects. Specific RGC types are lost if Brn3a is ablated during embryogenesis and only mildly affected by postnatal Brn3a ablation. Sparse but not complete Brn3a heterozygosity combined with complete Ret heterozygosity has striking effects on RGC type distribution. Brn3a only mildly modulates Ret transcription, while Ret knockouts exhibit slightly skewed Brn3a and Brn3b expression during development that is corrected by adult age. Brn3a loss of function modestly but significantly affects distribution of Ret co-receptors GFRα1-3, and neurotrophin receptors TrkA and TrkC in RGCs.

Conclusions

Based on these observations, we propose that Brn3a and Ret converge onto developmental pathways that control RGC type specification, potentially through a competitive mechanism requiring signaling from the surrounding tissue.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13064-021-00155-z.

Cell-type-specific binocular vision guides predation in mice

Predators use vision to hunt, and hunting success is one of evolution's main selection pressures. However, how viewing strategies and visual systems are adapted to predation is unclear. Tracking predator-prey interactions of mice and crickets in 3D, we find that mice trace crickets with their binocular visual fields and that monocular mice are poor hunters. Mammalian binocular vision requires ipsi- and contralateral projections of retinal ganglion cells (RGCs) to the brain. Large-scale single-cell recordings and morphological reconstructions reveal that only a small subset (9 of 40+) of RGC types in the ventrotemporal mouse retina innervate ipsilateral brain areas (ipsi-RGCs). Selective ablation of ipsi-RGCs (<2% of RGCs) in the adult retina drastically reduces the hunting success of mice. Stimuli based on ethological observations indicate that five ipsi-RGC types reliably signal prey. Thus, viewing strategies align with a spatially restricted and cell-type-specific set of ipsi-RGCs that supports binocular vision to guide predation.

Nonlinear Spatial Integration Underlies the Diversity of Retinal Ganglion Cell Responses to Natural Images

How neurons encode natural stimuli is a fundamental question for sensory neuroscience. In the early visual system, standard encoding models assume that neurons linearly filter incoming stimuli through their receptive fields, but artificial stimuli, such as contrast-reversing gratings, often reveal nonlinear spatial processing. We investigated to what extent such nonlinear processing is relevant for the encoding of natural images in retinal ganglion cells in mice of either sex.

How neurons encode natural stimuli is a fundamental question for sensory neuroscience. In the early visual system, standard encoding models assume that neurons linearly filter incoming stimuli through their receptive fields, but artificial stimuli, such as contrast-reversing gratings, often reveal nonlinear spatial processing. We investigated to what extent such nonlinear processing is relevant for the encoding of natural images in retinal ganglion cells in mice of either sex. We found that standard linear receptive field models yielded good predictions of responses to flashed natural images for a subset of cells but failed to capture the spiking activity for many others. Cells with poor model performance displayed pronounced sensitivity to fine spatial contrast and local signal rectification as the dominant nonlinearity. By contrast, sensitivity to high-frequency contrast-reversing gratings, a classical test for nonlinear spatial integration, was not a good predictor of model performance and thus did not capture the variability of nonlinear spatial integration under natural images. In addition, we also observed a class of nonlinear ganglion cells with inverse tuning for spatial contrast, responding more strongly to spatially homogeneous than to spatially structured stimuli. These findings highlight the diversity of receptive field nonlinearities as a crucial component for understanding early sensory encoding in the context of natural stimuli.

SIGNIFICANCE STATEMENT Experiments with artificial visual stimuli have revealed that many types of retinal ganglion cells pool spatial input signals nonlinearly. However, it is still unclear how relevant this nonlinear spatial integration is when the input signals are natural images. Here we analyze retinal responses to natural scenes in large populations of mouse ganglion cells. We show that nonlinear spatial integration strongly influences responses to natural images for some ganglion cells, but not for others. Cells with nonlinear spatial integration were sensitive to spatial structure inside their receptive fields, and a small group of cells displayed a surprising sensitivity to spatially homogeneous stimuli. Traditional analyses with contrast-reversing gratings did not predict this variability of nonlinear spatial integration under natural images.

A Neural Network Model With Gap Junction for Topological Detection

Visual information processing in the brain goes from global to local. A large volume of experimental studies has suggested that among global features, the brain perceives the topological information of an image first. Here, we propose a neural network model to elucidate the underlying computational mechanism. The model consists of two parts. The first part is a neural network in which neurons are coupled through gap junctions, mimicking the neural circuit formed by alpha ganglion cells in the retina. Gap junction plays a key role in the model, which, on one hand, facilitates the synchronized firing of a neuron group covering a connected region of an image, and on the other hand, staggers the firing moments of different neuron groups covering disconnected regions of the image. These two properties endow the network with the capacity of detecting the connectivity and closure of images. The second part of the model is a read-out neuron, which reads out the topological information that has been converted into the number of synchronized firings in the retina network. Our model provides a simple yet effective mechanism for the neural system to detect the topological information of images in ultra-speed.

New Features of Receptive Fields in Mouse Retina through Spike-triggered Covariance

Retinal ganglion cells (RGCs) encode various spatiotemporal features of visual information into spiking patterns. The receptive field (RF) of each RGC is usually calculated by spike-triggered average (STA), which is fast and easy to understand, but limited to simple and unimodal RFs. As an alternative, spike-triggered covariance (STC) has been proposed to characterize more complex patterns in RFs. This study compares STA and STC for the characterization of RFs and demonstrates that STC has an advantage over STA for identifying novel spatiotemporal features of RFs in mouse RGCs. We first classified mouse RGCs into ON, OFF, and ON/OFF cells according to their response to full-field light stimulus, and then investigated the spatiotemporal patterns of RFs with random checkerboard stimulation, using both STA and STC analysis. We propose five sub-types (T1–T5) in the STC of mouse RGCs together with their physiological implications. In particular, the relatively slow biphasic pattern (T1) could be related to excitatory inputs from bipolar cells. The transient biphasic pattern (T2) allows one to characterize complex patterns in RFs of ON/OFF cells. The other patterns (T3–T5), which are contrasting, alternating, and monophasic patterns, could be related to inhibitory inputs from amacrine cells. Thus, combining STA and STC and considering the proposed sub-types unveil novel characteristics of RFs in the mouse retina and offer a more holistic understanding of the neural coding mechanisms of mouse RGCs.

Molecular signatures of retinal ganglion cells revealed through single cell profiling

Retinal ganglion cells can be classified into more than 40 distinct subtypes, whether by functional classification or transcriptomics. The examination of these subtypes in relation to their physiology, projection patterns, and circuitry would be greatly facilitated through the identification of specific molecular identifiers for the generation of transgenic mice. Advances in single cell transcriptomic profiling have enabled the identification of molecular signatures for cellular subtypes that are only rarely found. Therefore, we used single cell profiling combined with hierarchical clustering and correlate analyses to identify genes expressed in distinct populations of Parvalbumin-expressing cells and functionally classified RGCs. RGCs were manually isolated based either upon fluorescence or physiological distinction through cell-attached recordings. Microarray hybridization and RNA-Sequencing were employed for the characterization of transcriptomes and in situ hybridization was utilized to further characterize gene candidate expression. Gene candidates were identified based upon cluster correlation, as well as expression specificity within physiologically distinct classes of RGCs. Further, we identified Prph, Ctxn3, and Prkcq as potential candidates for ipRGC classification in the murine retina. The use of these genes, or one of the other newly identified subset markers, for the generation of a transgenic mouse would enable future studies of RGC-subtype specific function, wiring, and projection.

Genetic access to neurons in the accessory optic system reveals a role for Sema6A in midbrain circuitry mediating motion perception

The accessory optic system (AOS) detects retinal image slip and reports it to the oculomotor system for reflexive image stabilization. Here, we characterize two Cre lines that permit genetic access to AOS circuits responding to vertical motion. The first (Pcdh9-Cre) labels only one of the four subtypes of ON direction-selective retinal ganglion cells (ON-DS RGCs), those preferring ventral retinal motion. Their axons diverge from the optic tract just behind the chiasm and selectively innervate the medial terminal nucleus (MTN) of the AOS. Unlike most RGC subtypes examined, they survive after optic nerve crush. The second Cre-driver line (Pdzk1ip1-Cre) labels postsynaptic neurons in the MTN. These project predominantly to the other major terminal nucleus of the AOS, the nucleus of the optic tract (NOT). We find that the transmembrane protein semaphorin 6A (Sema6A) is required for the formation of axonal projections from the MTN to the NOT, just as it is for the retinal innervation of the MTN. These new tools permit manipulation of specific circuits in the AOS and show that Sema6A is required for establishing AOS connections in multiple locations.